In the previous chapters, we introduced Markov chains and the Hidden Markov Model (HMM), and saw examples of modeling problems using them. In this chapter, we will see how we can make predictions using these models or ask the models questions (known as inference). The algorithms used for computing these values are known as inference algorithms. In this chapter, we will specifically look into computing probability distribution over the state variables.
This chapter will cover the following topics:
- State inference in HMM
- Dynamic programming
- Forward-backward algorithm
- Viterbi algorithm
